OpenRaiser/Intern-Atlas huggingface dataset:
Social Discrete Choice Models
Venue: arXiv cs.SI 2017
Year: 2017

Human decision making underlies data generating process in multiple application areas, and models explaining and predicting choices made by individuals are in high demand. Discrete choice models are widely studied in economics and computational social sciences. As digital social networking facilitates information flow and spread of influence between individuals, new advances in modeling are needed to incorporate social information into these models in addition to characteristic features affecting individual choices. In this paper, we propose two novel models with scalable training algorithms: local logistics graph regularization (LLGR) and latent class graph regularization (LCGR) models. We add social regularization to represent similarity between friends, and we introduce latent classes to account for possible preference discrepancies between different social groups. Training of the LLGR model is performed using alternating direction method of multipliers (ADMM), and training of the LCGR model is performed using a specialized Monte Carlo expectation maximization (MCEM) algorithm. Scalability to large graphs is achieved by parallelizing computation in both the expectation and the maximization steps. The LCGR model is the first latent class classification model that incorporates social relationships among individuals represented by a given graph. To evaluate our two models, we consider three classes of data to illustrate a typical large-scale use case in internet and social media applications. We experiment on synthetic datasets to empirically explain when the proposed model is better than vanilla classification models that do not exploit graph structure. We also experiment on real-world data, including both small scale and large scale real-world datasets, to demonstrate on which types of datasets our model can be expected to outperform state-of-the-art models.
Deeply-Supervised CNN for Prostate Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.
Visual Analyses of Music History: A User-Centric Approach
Venue: arXiv cs.HC 2017
Year: 2017

Music history, referring to the records of users' listening or downloading history in online music services, is the primary source for music service providers to analyze users' preferences on music and thus to provide personalized recommendations to users. In order to engage users into the service and to improve user experience, it would be beneficial to provide visual analyses of one user's music history as well as visualized recommendations to that user. In this paper, we take a user-centric approach to the design of such visual analyses. We start by investigating user needs on such visual analyses and recommendations, then propose several different visualization schemes, and perform a pilot study to collect user feedback on the designed schemes. We further conduct user studies to verify the utility of the proposed schemes, and the results not only demonstrate the effectiveness of our proposed visualization, but also provide important insights to guide the visualization design in the future.
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Venue: arXiv cs.LG 2017
Year: 2017

This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was developed to implement a robotics application consisting in sorting and storing objects in an unsupervised way. The toy dataset used to validate such application consists of Lego bricks of different shapes and colors. The uncontrolled lighting conditions together with the use of RGB color features, respectively involve data with a large spread and different levels of measurement between data dimensions. To overcome the combination of these two characteristics in the data, we have developed a new weighted K-means algorithm, called gap-ratio K-means, which consists in weighting each dimension of the feature space before running the K-means algorithm. The weight associated with a feature is proportional to the ratio of the biggest gap between two consecutive data points, and the average of all the other gaps. This method is compared with two other variants of K-means on the Lego bricks clustering problem as well as two other common classification datasets.
Energy-Efficient Data Transfers in Radio Astronomy with Software UDP RDMA
Venue: arXiv cs.NI 2017
Year: 2017

Modern radio astronomy relies on very large amounts of data that need to be transferred between various parts of astronomical instruments, over distances that are often in the range of tens or hundreds of kilometres. The Square Kilometre Array (SKA) will be the world's largest radio telescope, data rates between its components will exceed Terabits per second. This will impose a huge challenge on its data transport system, especially with regard to power consumption. High-speed data transfers using modern off-the-shelf hardware may impose a significant load on the receiving system with respect to CPU and DRAM usage. The SKA has a strict energy budget which demands a new, custom-designed data transport solution. In this paper we present SoftiWARP UDP, an unreliable datagram-based Remote Direct Memory Access (RDMA) protocol, which can significantly increase the energy-efficiency of high-speed data transfers for radio astronomy. We have implemented a fully functional software prototype of such a protocol, supporting RDMA Read and Write operations and zero-copy capabilities. We present measurements of power consumption and achieved bandwidth and investigate the behaviour of all examined protocols when subjected to packet loss.
Characterization of Deterministic and Probabilistic Sampling Patterns for Finite Completability of Low Tensor-Train Rank Tensor
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we analyze the fundamental conditions for low-rank tensor completion given the separation or tensor-train (TT) rank, i.e., ranks of unfoldings. We exploit the algebraic structure of the TT decomposition to obtain the deterministic necessary and sufficient conditions on the locations of the samples to ensure finite completability. Specifically, we propose an algebraic geometric analysis on the TT manifold that can incorporate the whole rank vector simultaneously in contrast to the existing approach based on the Grassmannian manifold that can only incorporate one rank component. Our proposed technique characterizes the algebraic independence of a set of polynomials defined based on the sampling pattern and the TT decomposition, which is instrumental to obtaining the deterministic condition on the sampling pattern for finite completability. In addition, based on the proposed analysis, assuming that the entries of the tensor are sampled independently with probability $p$, we derive a lower bound on the sampling probability $p$, or equivalently, the number of sampled entries that ensures finite completability with high probability. Moreover, we also provide the deterministic and probabilistic conditions for unique completability.
Graffinity: Visualizing Connectivity In Large Graphs
Venue: arXiv cs.GR 2017
Year: 2017

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.
Oops!...I think I scanned a malware
Venue: arXiv cs.CR 2017
Year: 2017

This article presents a proof-of-concept illustrating the feasibility of creating a covert channel between a C\&C server and a malware installed in an organization by exploiting an organization's scanner and using it as a means of interaction. We take advantage of the light sensitivity of a flatbed scanner, using a light source to infiltrate data to an organization. We present an implementation of the method for different purposes (even to trigger a ransomware attack) in various experimental setups using: (1) a laser connected to a stand (2) a laser carried by a drone, and (3) a hijacked smart bulb within the targeted organization from a passing car. In our experiments we were able to infiltrate data using different types of light sources (including infrared light), from a distance of up to 900 meters away from the scanner. We discuss potential counter measures to prevent the attack.
Width Hierarchies for Quantum and Classical Ordered Binary Decision Diagrams with Repeated Test
Venue: arXiv cs.CC 2017
Year: 2017

We consider quantum, nondterministic and probabilistic versions of known computational model Ordered Read-$k$-times Branching Programs or Ordered Binary Decision Diagrams with repeated test ($k$-QOBDD, $k$-NOBDD and $k$-POBDD). We show width hierarchy for complexity classes of Boolean function computed by these models and discuss relation between different variants of $k$-OBDD.
Toward Traffic Patterns in High-speed Railway Communication Systems: Power Allocation and Antenna Selection
Venue: arXiv cs.IT 2017
Year: 2017

In high-speed railway (HSR) communication systems, distributed antenna is usually employed to support frequent handover and enhance the signal to noise ratio to user equipments. In this case, dynamic time-domain power allocation and antenna selection (PAWAS) could be jointly optimized to improve the system performances. This paper consider this problem in such a simple way where dynamic switching between multiple-input-multiple-output (MIMO) and single-input-multiple-output (SIMO) is allowed and exclusively utilized, while the channel states and traffic demand are taken into account. The channel states includes sparse and rich scattering terrains, and the traffic patterns includes delay-sensitive and delay-insensitive as well as hybrid. Some important results are obtained in theory. In sparse scattering terrains, for delay-sensitive traffic, the PAWAS can be viewed as the generalization of channel-inversion associated with transmit antenna selection. On the contrary, for delay-insensitive traffic, the power allocation with MIMO can be viewed as channel-inversion, but with SIMO, it is traditional water-filling. For the hybrid traffic, the PAWAS can be partitioned as delay-sensitive and delay-insensitive parts by some specific strategies. In rich scattering terrains, the corresponding PAWAS is derived by some amendments in sparse scattering terrains and similar results are then presented.
Nonlinear Spectral Image Fusion
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks. The well-localized and edge-preserving spectral TV decomposition allows to select frequencies of a certain image to transfer particular features, such as wrinkles in a face, from one image to another. We illustrate the effectiveness of the proposed approach in several numerical experiments, including a comparison to the competing techniques of Poisson image editing, linear osmosis, wavelet fusion and Laplacian pyramid fusion. We conclude that the proposed spectral TV image decomposition framework is a valuable tool for semi- and fully-automatic image editing and fusion.
Indoor Office Wideband Penetration Loss Measurements at 73 GHz
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents millimeter wave (mmWave) penetration loss measurements and analysis at 73 GHz using a wideband sliding correlator channel sounder in an indoor office environment. Penetration loss was measured using a carefully controlled measurement setup for many common indoor building materials such as glass doors, glass windows, closet doors, steel doors, and whiteboard writing walls. Measurements were conducted using narrowbeam transmitter (TX) and receiver (RX) horn antennas that were boresight-aligned with a test material between the antennas. Overall, 21 different locations were measured for 6 different materials such that the same type of material was tested in at least two locations in order to characterize the effect of penetration loss for materials with similar composition. As shown here, attenuation through common materials ranged between 0.8 dB/cm and 9.9 dB/cm for co-polarized antennas, while cross-polarized antennas exhibited similar attenuation for most materials, but up to 23.4 dB/cm of attenuation for others. The penetration loss results presented here are useful for site-specific planning tools that will model indoor mmWave networks, without the need for expensive measurement campaigns.
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
Venue: arXiv cs.DS 2017
Year: 2017

This thesis is in the area called computational social choice which is an intersection area of algorithms and social choice theory.
Recurrent and Contextual Models for Visual Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

We propose a series of recurrent and contextual neural network models for multiple choice visual question answering on the Visual7W dataset. Motivated by divergent trends in model complexities in the literature, we explore the balance between model expressiveness and simplicity by studying incrementally more complex architectures. We start with LSTM-encoding of input questions and answers; build on this with context generation by LSTM-encodings of neural image and question representations and attention over images; and evaluate the diversity and predictive power of our models and the ensemble thereof. All models are evaluated against a simple baseline inspired by the current state-of-the-art, consisting of involving simple concatenation of bag-of-words and CNN representations for the text and images, respectively. Generally, we observe marked variation in image-reasoning performance between our models not obvious from their overall performance, as well as evidence of dataset bias. Our standalone models achieve accuracies up to $64.6\%$, while the ensemble of all models achieves the best accuracy of $66.67\%$, within $0.5\%$ of the current state-of-the-art for Visual7W.
Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions
Venue: arXiv cs.CV 2017
Year: 2017

Characterization of breast lesions is an essential prerequisite to detect breast cancer in an early stage. Automatic segmentation makes this categorization method robust by freeing it from subjectivity and human error. Both spectral and morphometric features are successfully used for differentiating between benign and malignant breast lesions. In this thesis, we used empirical mode decomposition method for semi-automatic segmentation. Sonographic features like ehcogenicity, heterogeneity, FNPA, margin definition, Hurst coefficient, compactness, roundness, aspect ratio, convexity, solidity, form factor were calculated to be used as our characterization parameters. All of these parameters did not give desired comparative results. But some of them namely echogenicity, heterogeneity, margin definition, aspect ratio and convexity gave good results and were used for characterization.
A Universal Construction for (Co)Relations
Venue: arXiv cs.LO 2017
Year: 2017

Calculi of string diagrams are increasingly used to present the syntax and algebraic structure of various families of circuits, including signal flow graphs, electrical circuits and quantum processes. In many such approaches, the semantic interpretation for diagrams is given in terms of relations or corelations (generalised equivalence relations) of some kind. In this paper we show how semantic categories of both relations and corelations can be characterised as colimits of simpler categories. This modular perspective is important as it simplifies the task of giving a complete axiomatisation for semantic equivalence of string diagrams. Moreover, our general result unifies various theorems that are independently found in literature, including the cases of linear corelations (relevant for the semantics of electrical circuits), of partial equivalence relations and of linear subspaces (semantics of signal flow graphs and of the phase-free ZX calculus).
The pdf-mpc Package: A Free-Matlab-Coder package for Real-Time Nonlinear Model Predictive Control
Venue: arXiv cs.SY 2017
Year: 2017

This paper describes the Parametrized Derivative-Free Model Predictive Control pdf-mpc package, a matlab coder-based set of subroutines that enables a model predictive control problem to be defined and solved. the pdf-mpc is made available for free download and use through the website of the author..
Supervisor Synthesis of POMDP based on Automata Learning
Venue: arXiv cs.SY 2017
Year: 2017

As a general and thus popular model for autonomous systems, partially observable Markov decision process (POMDP) can capture uncertainties from different sources like sensing noises, actuation errors, and uncertain environments. However, its comprehensiveness makes the planning and control in POMDP difficult. Traditional POMDP planning problems target to find the optimal policy to maximize the expectation of accumulated rewards. But for safety critical applications, guarantees of system performance described by formal specifications are desired, which motivates us to consider formal methods to synthesize supervisor for POMDP. With system specifications given by Probabilistic Computation Tree Logic (PCTL), we propose a supervisory control framework with a type of deterministic finite automata (DFA), za-DFA, as the controller form. While the existing work mainly relies on optimization techniques to learn fixed-size finite state controllers (FSCs), we develop an $L^*$ learning based algorithm to determine both space and transitions of za-DFA. Membership queries and different oracles for conjectures are defined. The learning algorithm is sound and complete. An example is given in detailed steps to illustrate the supervisor synthesis algorithm.
An Asymptotically Tighter Bound on Sampling for Frequent Itemsets Mining
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we present a new error bound on sampling algorithms for frequent itemsets mining. We show that the new bound is asymptotically tighter than the state-of-art bounds, i.e., given the chosen samples, for small enough error probability, the new error bound is roughly half of the existing bounds. Based on the new bound, we give a new approximation algorithm, which is much simpler compared to the existing approximation algorithms, but can also guarantee the worst approximation error with precomputed sample size. We also give an algorithm which can approximate the top-$k$ frequent itemsets with high accuracy and efficiency.
Multi-Level Discovery of Deep Options
Venue: arXiv cs.LG 2017
Year: 2017

Augmenting an agent's control with useful higher-level behaviors called options can greatly reduce the sample complexity of reinforcement learning, but manually designing options is infeasible in high-dimensional and abstract state spaces. While recent work has proposed several techniques for automated option discovery, they do not scale to multi-level hierarchies and to expressive representations such as deep networks. We present Discovery of Deep Options (DDO), a policy-gradient algorithm that discovers parametrized options from a set of demonstration trajectories, and can be used recursively to discover additional levels of the hierarchy. The scalability of our approach to multi-level hierarchies stems from the decoupling of low-level option discovery from high-level meta-control policy learning, facilitated by under-parametrization of the high level. We demonstrate that using the discovered options to augment the action space of Deep Q-Network agents can accelerate learning by guiding exploration in tasks where random actions are unlikely to reach valuable states. We show that DDO is effective in adding options that accelerate learning in 4 out of 5 Atari RAM environments chosen in our experiments. We also show that DDO can discover structure in robot-assisted surgical videos and kinematics that match expert annotation with 72% accuracy.
Laderman matrix multiplication algorithm can be constructed using Strassen algorithm and related tensor's isotropies
Venue: arXiv cs.SC 2017
Year: 2017

In 1969, V. Strassen improves the classical~2x2 matrix multiplication algorithm. The current upper bound for 3x3 matrix multiplication was reached by J.B. Laderman in 1976. This note presents a geometric relationship between Strassen and Laderman algorithms. By doing so, we retrieve a geometric formulation of results very similar to those presented by O. Sykora in 1977.
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
Venue: arXiv cs.AI 2017
Year: 2017

A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional method which we call Smart Augmentation and we show how to use it to increase the accuracy and reduce overfitting on a target network. Smart Augmentation works by creating a network that learns how to generate augmented data during the training process of a target network in a way that reduces that networks loss. This allows us to learn augmentations that minimize the error of that network. Smart Augmentation has shown the potential to increase accuracy by demonstrably significant measures on all datasets tested. In addition, it has shown potential to achieve similar or improved performance levels with significantly smaller network sizes in a number of tested cases.